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Deep learning for whole slide image analysis : an overview

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Dimitriou_2019_FM_Deeplearning_CC.pdf (639.6Kb)
Date
22/11/2019
Author
Dimitriou, Neofytos
Arandjelović, Ognjen
Caie, Peter D.
Keywords
Digital pathology
Computer vision
Oncology
Cancer
Machine learning
Personalized pathology
Image analysis
RC0254 Neoplasms. Tumors. Oncology (including Cancer)
QA75 Electronic computers. Computer science
SDG 3 - Good Health and Well-being
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Abstract
The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images have billions of pixels and suffer from high morphological heterogeneity as well as from different types of artifacts. Collectively, these impede the conventional use of deep learning. For the clinical translation of deep learning solutions to become a reality, these challenges need to be addressed. In this paper, we review work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlight the different ideas underlying these methodologies.
Citation
Dimitriou , N , Arandjelović , O & Caie , P D 2019 , ' Deep learning for whole slide image analysis : an overview ' , Frontiers in Medicine , vol. 6 , 264 . https://doi.org/10.3389/fmed.2019.00264
Publication
Frontiers in Medicine
Status
Peer reviewed
DOI
https://doi.org/10.3389/fmed.2019.00264
ISSN
2296-858X
Type
Journal item
Rights
Copyright © 2019 Dimitriou, Arandjelović and Caie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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  • University of St Andrews Research
URL
https://www.frontiersin.org/articles/10.3389/fmed.2020.00419/full
URI
http://hdl.handle.net/10023/19028

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